Truthound Depot Data/AI Governance
How Truthound Depot connects dataset validation, AI training image workflows, Private Agent execution, ERD/DB audits, and Evidence-based review.
Definition
Truthound Depot governs sensitive data and AI validation work by keeping source execution inside a safe boundary while preserving the summaries, metadata, validation outcomes, reviewer decisions, and Evidence needed for repeat review.
Problem
Teams are asked to approve dataset changes, private image assets, and database implementations without moving sensitive material outside. They need proof of what was checked, why it passed or failed, and whether the approved design matches the implemented database.
Styxis perspective
Styxis builds software for sensitive data validation and AI governance, guided by Oath, Boundary, and Crossing: it binds responsibility to action, distinguishes operating states, and moves verified change into execution.
Product connection
Truthound Depot organizes six related surfaces: dataset validation and versioning, AI training image validation and generation workflow metadata, Private Agent safe execution boundaries, ERD design audit, DB implementation audit, and Evidence-based reports.
FAQ
What is Truthound Depot?
Truthound Depot is a Data/AI Governance product for dataset validation, AI training image workflows, and ERD/DB audits. It keeps sensitive execution in safe environments and records reviewable summaries, metadata, and Evidence.
Does sensitive data leave the customer boundary?
The product is not designed to export original data, database credentials, or private images as product data. Private Agent execution keeps credentials and source access local by default and returns reviewable result contracts, summaries, metadata, and Evidence.
What does the Private Agent process?
A Private Agent runs validation close to the source with local credentials, TLS, and read-only access. It can process dataset checks, PII and schema validation, source-scoped quality results, and DB inspection needed for audit evidence.
What does ERD audit verify?
ERD audit checks whether the design follows approved standard words, standard terms, domains, naming rules, and relationship intent before the design is treated as approved.
How does DB implementation validation work?
DB implementation validation compares the actual database with the approved ERD, including physical names, logical names, tables, columns, keys, relationships, and structure.
How far do dataset review and reports go?
Truthound Depot supports dataset validation, version comparison, reviewer decisions, Evidence, and readable reports that explain what was checked and why a change was approved, rejected, or restored.
What does the AI training image workflow validate?
It helps review image quality, annotation consistency, generation workflow metadata, and dataset readiness while keeping private image assets inside the agreed execution boundary.